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With the widespread application of lithium-ion battery packs, it calls for efficient balancing methods to improve the performance of these battery systems. The relevant research has focused on the design of equalization circuits and control strategies while neglecting quantitative analysis and comparison of battery active equalization structures from the systematic level. In this paper, we propose an improved system-theoretic modeling approach for active equalization structures that takes into account the battery's constraints, including equalization current rate and SOC operation range. Accordingly, the mathematical models of various active equalization structures are derived that can describe their equalization dynamic behavior. Then, the analytical algorithms are developed to estimate the balancing time of active equalization structures online. Finally, extensive numerical simulations are conducted to evaluate and compare the average performance of these equalization systems in term of equalization time. It then comes up with a comprehensive comparison among them. Consequently, the Multicell-to-Multicell (MC2MC) balancing structure benefits the fast balancing speed and the lowest sensitive to initial batteries’ SOCs. The global structure has the lowest implementation cost and the series structure benefits the simplest control. Moreover, the module-based structures have faster balancing speed when each module contains about 4–6 batteries. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]

Long horizon state of health (SOH) monitoring and remaining useful life (RUL) prediction are of industrial value in prognostic and health management (PHM) of lithium-ion batteries (LIBs) to ensure their reliable functionality by early detection. Machine Learning, as a data-driven health diagnostic technique, has been widely utilized in solitary and hybrid structures. However, an accurate SOH estimation and RUL prediction method with less computational burden are highly desirable for the online state prediction in an electric vehicle application. This paper evaluates nonlinear autoregressive with external input (NARX) recurrent neural network (RNN) and time delay neural network (TDNN) in their prediction precision using the NASA dataset. The superior method, NARXRNN, is employed for two different datasets to estimate the battery's SOH and predict its RUL on a broad horizon. The results reveal the outstanding performance by presenting the root mean square error within 3% and mean absolute error within 2% for unseen data. Therefore, this method is capable to accurately predict the SOH of LIBS from historical data at low computational complexity. It is a promising model for long horizon SOH and RUL prediction and practical for online applications. [ABSTRACT FROM AUTHOR]

This paper proposes a new fast chargingstrategy for lithium-ion (Li-ion) batteries. The approach relies on an experimentally validated high-fidelity model describing battery electrochemical and thermal dynamics that determine the fast charging capability. Such a high-dimensional nonlinear dynamic model can be intractable to compute in real-time if it is fused with the extended Kalman filter or the unscented Kalman filter that is commonly used in the community of battery management. To significantly save computational efforts and achieve rapid convergence, the ensemble transform Kalman filter (ETKF) is selected and tailored to estimate the nonuniform Li-ion battery states. Then, a health- and safety-aware charging protocol is proposed based on successively applied proportional-integral (PI) control actions. The controller regulates charging rates using online battery state information and the imposed constraints, in which each PI control action automatically comes into play when its corresponding constraint is triggered. The proposed physical constraint-triggered PI charging control strategy with the ETKF is evaluated and compared with several prevalent alternatives. It shows that the derived controller can achieve close to the optimal solution in terms of charging time and trajectory, as determined by a nonlinear model predictive controller, but at a drastically reduced computational cost. [ABSTRACT FROM AUTHOR]

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated. [ABSTRACT FROM AUTHOR]

For timely maintenance and replacement in lithium-ion battery system, it is crucial to achieve fast and accurate State of Health (SOH) prediction. SOH is a dynamic status parameter of a battery indicated by its available capacity compared to the initial condition. SOH tends to decrease in the long-term because a battery is aged by its charging and discharging cycles due to the degradation of chemical composition. To ensure a battery has sufficiently good conditions, it is significant to predict the battery SOH precisely so that the depleted or weak battery can be replaced in time. However, the prediction of battery SOH is a difficult and complex task due to the following reasons (1) SOH degradation is a dynamic process because of the time-varying nature of both battery electrochemistry and working condition; (2) SOH prediction model usually involves numerous input attributes that lead to sheer complexity and computational cost for real-time battery management; (3) the balance of overhead-accuracy tradeoff is challenging to adapt to various application scenarios. In this paper, we propose a theoretical framework for fast and accurate SOH prediction based on the non-additive measure and overhead-accuracy balancing. Specifically, we characterize the interactions among battery attributes and their aggregated impacts on SOH prediction. By choosing the most significant subset of battery attributes, the computational complexity of SOH prediction can be substantially reduced while maintaining high accuracy. The tradeoff between accuracy and computational overhead can be balanced by adjusting the number of attributes and volume of training data. The experimental results validate the effectiveness and efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

The thermal characteristics of a battery are vital in order to understand its health and safety. The thermal characteristics are influenced by charging techniques. This paper presents theoretical explanation, experimental validation, and an analytical method to quantify the heat loss influenced by charging techniques for a Li-Ion battery. Constant current constant voltage (CC-CV), sinusoidal ripple current constant voltage, and pulse current constant voltage charging profiles are used as test cases for validation. The electrical and entropic heat generation for different charging profiles are modeled and estimated based on battery equivalent parameters. The estimated heat generation is compared with the heat flux measurement from a differential scanning calorimeter. In addition to the heat flux measurement, temperature, state of charge, terminal voltage, and current were recorded and analyzed. [ABSTRACT FROM AUTHOR]

A high-fidelity battery model capable of accurately predicting real-time battery behavior for various operating conditions is crucial for the design and operation of battery-powered systems. Based upon a prior work of the authors, this paper proposes an enhanced hybrid battery model to provide accurate predictions for the runtime behaviors of rechargeable electrochemical battery cells under various ambient temperatures and charge/discharge current conditions. The proposed battery model is validated by simulation and experimental studies for lithium-ion battery cells in various current and temperature conditions. The proposed battery model is computationally effective for design, simulation, condition monitoring, and power management of various battery systems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel fast open circuit voltage prediction approach for Lithium-ion battery, which is potential to facilitate a convenient battery modeling and states estimation in the energy storage system. Open circuit voltage measurement suffers from a long relaxation time (several hours, even days) to reach the thermodynamic equilibrium of the battery. On the basis of the feedback control theory, the proposed multiple correction approach utilizes the constrained nonlinear optimization of the power function in each curve fitting step. The voltage measurement in a short period is divided into several segments to correct the voltage prediction multiple times with the feedback errors after each curve fitting. The similarity between the shape of the power function and the variation of the terminal voltage during the relaxation time is utilized. The proposed method can speed up the time-consuming open circuit voltage measurement and predict the open circuit voltage with high accuracy. Experimental tests on a LiFePO4 battery prove the validation and effectiveness of the proposed method in accurately predicting the open circuit voltage within a very short relaxation time (less than 15 min). [ABSTRACT FROM AUTHOR]

The efficiency of a proton exchange membrane fuel cell system (PEMFCS), which includes a PEMFC stack and a dc/dc converter, depends on the applied voltage and there exists an efficiency extreme point (EEP) in PEMFCS. Therefore, the EEP of PEMFCS changes with varying load, because the system parameters are also varying. It makes EEP tracking (EEPT) a challenging assignment. In order to achieve the EEP operation of PEMFCS under varying load conditions without oscillation, this paper proposes a novel EEPT method based on a forgetting factor recursive least square (FFRLS) online identification. The FFRLS algorithm is based on data-driven and uses real-time data of the system to improve the estimation accuracy of PEMFCS parameters under the changing load. On the test bench of PEMFCS-battery system consists of 300-W PEMFCS, lithium-ion battery, PEMFC controller, and DSP controller, a multi-index performance test and comparative analysis were carried out. The results showed that the proposed method is not sensitive to load conditions. The performance of the proposed EEPT method for PEMFCS has been successfully validated. In addition, compared with the “perturb and observe” and “incremental conductance” online tracking algorithms, the proposed online identification method can get more satisfactory results. Such as, reducing the tracking time and hydrogen consumption, making the output power of PEMFCS smoother, which means its operation stress is smaller, and thus, it lasts longer. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power production ramp-rate control is getting more and more important in weak electric power systems, in which quick and significant power fluctuations can affect the stability of the system. This can be achieved by means of the integration of batteries into large PV plants but such an operation involves an aggressive environment for the ageing of the batteries. This paper presents an evaluation of this ageing by means of the annual simulations of a large PV power plant using actual irradiance data. This is done for different battery sizes used under various degrees of limitation in the power ramp-rate variation. The levelized cost of storage is calculated for each of the cases considered. [ABSTRACT FROM AUTHOR]

Modeling of battery energy storage systems used for applications, such as electric vehicles and smart grids, emerged as a necessity over the last decade and depends heavily on the accurate estimation of battery states and parameters. Depending on the battery-cell type and operation, a combination of algorithms is used to identify battery parameters and define battery states. This paper deals with robust Li-ion batteries modeling with a specific focus on a hybrid approach for a more accurate state-of-charge (SOC) estimation. The analysis presents a detailed description of the state-of-the-art stand-alone SOC estimation methods and focuses on a hybrid SOC estimation technique to improve accuracy under varying conditions. Emphasis is given on performance improvements of the proposed hybrid approach compared to the conventional methods, whereas a thorough experimental validation is presented to evaluate the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The neglect of the history depreciation imbalance in the conventional equalization strategies may aggravate the lifetime depreciation of the multi-battery energy storage systems (MBESSs) and overuse the high-history-depreciation batteries (HHDBs). This paper proposes an equalization strategy using maximum consistency tracking algorithm of the conditional depreciation to solve the stated problem. Conditional depreciation is innovatively defined to evaluate the operation equitability of the MBESSs. The maximum consistency tracking algorithm consists of two searching levels: the global searching level and the range searching level. A sequential approximation method is utilized at the global searching level to find the variation range consisting of the most equitable conditional depreciation. The newton downhill method is adopted at the range searching level to find the exact value of the most equitable conditional depreciation. The proposed equalization strategy is validated by a hardware-in-the-loop simulation system. Test results show that the proposed equalization strategy minimizes the imbalance of batteries’ conditional depreciations and decreases the HHDBs’ sharing power. Compared to the conventional equalization strategies, the proposed equalization strategy will protect the HHDBs from intermittent overuse and extend the lifetime of the MBESSs. [ABSTRACT FROM AUTHOR]

In this paper, a method is introduced to find out the optimal charge pattern (OCP) of the multistage constant current (MSCC) charge method based on the equivalent circuit model of a Li-ion battery. Unlike the conventional methods, the proposed one does not require several kinds of time-consuming and labor-intensive experiments. The OCP can be simply calculated by the developed equations with the results obtained by a simple experiment. The validity of the proposed method has been verified through the experimental results and it shows that the charge time and the charge energy can be reduced by 12% and 1.8%, respectively, and the charge efficiency of a Li-ion battery can be improved by 0.54% as compared to the conventional constant current–constant voltage (CC–CV) method. A lower temperature rise during the charge can be regarded as an additional advantage of the proposed method. [ABSTRACT FROM AUTHOR]

Temperature is one of the key factors when working with lithium-ion battery modules due to its influence in safety, performance, and lifespan concerns in these devices. High working temperatures reduce the available capacity of each cell within the module after several cycles due to aging; nonuniform temperature distributions in the module lead to different aging processes in each cell, and thus it will be impossible to take advantage of all the energy available in the Li-ion storage system, because the most aged cell will limit the available energy. This paper is focused on the minimization of the maximum temperature gradient of a battery module for high depth of discharge applications to avoid different aging processes of the cells within the module. Voltage balancing is proposed as a solution for this purpose, and different strategies and their results are presented in this paper. [ABSTRACT FROM AUTHOR]

The least cell state-of-charge (SOC) in a battery pack directly determines the left runtime of the total battery pack. Hence, an equalizing strategy is often needed to balance the battery cells, especially for lithium-ion batteries. In this paper, it is shown that this can be achieved with a simple equalizing circuit, which only needs a power semiconductor switch and an inductor for each cell. To avoid the intensive requirement on the processing power of existing control algorithms, two simple control schemes are proposed to obtain better modular performance. First, the operating modes under different switching states are illustrated with a four-cell battery module. After that, the required duty cycles to operate the switches are obtained by comparing the SOC of the relevant cells and the average SOC of all cells. By fixing the maximum of the duty cycle of the highest SOC cell, the inductor can be easily designed and the rated currents of switches and cells can also be regulated. Moreover, the multilayer and modular concept is developed to speed up the balancing process and modularize the design of equalizing circuits. Finally, experiments were completed to demonstrate the proposed scheme. [ABSTRACT FROM AUTHOR]

The growing need for accurate estimation of by fitting battery's thermal and electrical performances at different operating conditions is crucial in its applications especially in electrified vehicles. This paper presents an effective method for developing a thermal and electrical modeling methodology for calculation thermal behavior of a lithium-ion cell and the voltage response under a current solicitation. The model was elaborated on three pouch cells with different battery chemistries for use in electrical vehicles/hybrid electrical vehicles, namely lithium iron phosphate, lithium nickel manganese cobalt oxide, and lithium titanium oxide (LTO). The model, implemented in a MATLAB/Simulink interface, uses an equivalent circuit and heat-generation equations coupled a thermal model. The three cell chemistries have been investigated using test procedures and thermal images at room temperature. The results of this study show that a temperature distribution to be fairly uniform after a complete discharge for the three chemistries with the lowest temperature gradient found for the LTO-based cell. Finally, comparison between simulation results and measured data under dynamic profiles shows a good correspondence with the measurements of the validation tests with errors lying between ±4% and 2 °C for the electrical and thermal model, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a standard and flexible test bench architecture for the testing and modeling of electrochemical energy modules. It also presents a comprehensive methodology that enables to obtain accurate models under realistic operating conditions with a low computational cost. For this purpose, three different configuration modes of the experimental architecture setup are introduced. Finally, the test bench architecture and the experimental methodology proposed have been validated by the modeling and testing of a Li-ion battery pack and a supercapacitor module. [ABSTRACT FROM PUBLISHER]

This paper proposes an evaluation method for equivalent circuit model of lithium polymer battery (LiPB), which is widely used in the hybrid electric vehicle (HEV), applying the Bode plot and the numerical method. The accurate circuit parameters are determined by numerical analysis and transfer function that is evaluated from the frequency response of LiPB. Generally, the inductive component of battery has an unexpected influence. However, this method takes account of the effect of increased inductive reactance by the high switching frequency. Moreover, the numerical analysis makes the inductive and capacitive components independent of each other, which results in the more accurate circuit parameters of LiPB. The propriety of the evaluation method proposed in this paper is verified by comparison between the voltage–current profiles of battery equipped in the real HEV and that of simulation result using the circuit parameters with respect to the state of charge (SOC) and frequency. [ABSTRACT FROM PUBLISHER]

The maximum capacity, the amount of maximal electric charge that a battery can store, not only indicates the state of health, but also is required in numerous methods for state-of-charge estimation. This paper proposes an alternative approach to perform online estimation of the maximum capacity by solving the recursive total-least-squares (RTLS) problem. Different from prior art, the proposed approach poses and solves the RTLS as a Rayleigh quotient optimization problem. The Rayleigh quotient-based approach can be readily generalized to other parameter estimation problems including impedance estimation. Compared with other capacity estimation methods, the proposed algorithm enjoys the advantages of existing RTLS-based algorithms for instance, low computation, simple implementation, and high accuracy, and thus is suitable for use in real-time embedded battery management systems. The proposed method is compared with existing methods via simulations and experiments. [ABSTRACT FROM AUTHOR]

This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers’ data. The battery profiles of different manufacturers’ like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers’ catalogue) characteristics. [ABSTRACT FROM PUBLISHER]

Effective heat transfer and protection against high operating temperatures are important, lifetime-affecting items in the design and operation of batteries, especially those intended for hybrid mobile working machines, where the peak power and current demands are high. This paper describes a methodology for the modeling of the electrical and thermal behavior of lithium ion pouch cells for hybrid mobile working machines. The model is produced by coupling the equivalent electrical circuit of the pouch cell and the 3-D pouch cell thermal model. The temperature dependence of the battery operation parameters is added to the model in order to analyze the influence of temperature on heat generation during battery operation. Determination of the parameters of the thermal model is carried out empirically based on the presented methodology, which allows the construction of a 3-D thermal model of a pouch cell without requiring detailed information about the battery chemistry. The validity of the modeling procedure is demonstrated experimentally for a 60 Ah lithium titanate pouch cell. [ABSTRACT FROM PUBLISHER]

Lithium-ion (Li-ion) batteries have emerged as the major power source for today's electronic products because they can offer advantages such as high energy density, low maintenance requirement, high open-circuit voltage, low self-discharge rate, and lack of memory effect. The performance and lifespan of Li-ion batteries are closely related to the quality of the charging pattern. Therefore, an optimal charging pattern is essential for Li-ion batteries to achieve shorter charging time and longer cycle life. In this paper, a Taguchi-based algorithm is presented. Consecutive orthogonal array techniques are utilized to determine the optimal charging pattern for the five-step constant current charging method. The problem formulation, implementation procedure, and parameter setting method are described and explained in detail. Experimental results show that the obtained charging pattern is capable of charging the Li-ion batteries to 95% capacity. Comparing to the conventional constant current–constant voltage method, the benefits of the obtained pattern are faster and safer charging and longer battery cycle life. [ABSTRACT FROM AUTHOR]

All electrochemical batteries are characterized by an electrical behavior that depends on temperature, state of charge, and current. In the case of lithium-ion accumulators, the energetical behavior is moreover deeply marked by line effects, due to the porosity of both electrodes. This paper deals with investigations on an accurate energetical modeling of lithium-ion battery. It is shown in particular that electrode porosity can be taken into account by means of a diffusion impedance represented by a capacitive transmission line. An energetical model, which couples this line with a current independent capacitance, is proposed and characterized at constant temperature (20 ^\circC) over different state-of-charge intervals (5%) and for different currents. Reactant diffusion within the electrolyte and relaxation period after discharge are investigated as well. Validation tests carried out on a 6.8-Ah lithium-ion element are conclusive. [ABSTRACT FROM PUBLISHER]